A High-Performance GriFTP Server at Desktop Cost
Bibliographic record
Abstract
Abstract-- We prototype a storage system that provides the access performance of a well-endowed GridFTP deployment (e.g., using a cluster and a parallel file-system) at the modest cost of single desktop. To this end, we integrate GridFTP and a combination of dedicated but low-bandwidth (thus cheap) storage nodes and scavenged storage from LAN-connected desktops that participate intermittently to the storage pool. The main advantage of this setup is that it alleviates the server I/O access bottleneck. Additionally, the specific data access pattern of GridFTP, that is, the fact that data accesses are mostly sequential, allows for optimizations that result in a high-performance storage system. To provide data durability when facing intermittent participation of the storage resources, we use an intelligent replication scheme that minimizes the volume of internal transfers that impact the low-bandwidth storage nodes. The Problem. GridFTP [1] extends the FTP protocol with new features such as striping and partial file access. GridFTP has become the data access and management protocol of choice for Grid deployments in data-intensive scientific communities. As a result, significant efforts have been made to optimize the protocol itself and the software stack implementing it, and, more relevant to our work, often, GridFTP deployments are supported by expensive hardware resources that enable high storage I/O access rates (e.g., clusters and parallel file systems). The protocol has been itself modified in order to be able to take
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".